Latent Semantic Analysis

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.


Reference manual

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install.packages("lsa")

0.73.4 by Fridolin Wild, 9 months ago


Browse source code at https://github.com/cran/lsa


Authors: Fridolin Wild [aut, cre]


Documentation:   PDF Manual  


GPL (>= 2) license


Depends on SnowballC

Suggests tm


Imported by DTWBI, DTWUMI, GeneNMF, IBCF.MTME, MD2sample, OmicsQC, OutSeekR, SemanticDistance, WordListsAnalytics, conversim.

Depended on by AurieLSHGaussian, LSAfun.

Suggested by MosaiClusteR, Signac, huggingfaceR, quanteda, quanteda.textmodels.


See at CRAN